Chang Chia Liu

A Novel Wavelet Based Algorithm for Spike and Wave Detection in Absence Epilepsy

Absence seizures are characterized by sudden loss of consciousness and interruption of ongoing motor activities for a brief period of time lasting few to several seconds and up to half a minute. Due to their brevity and subtle clinical manifestations absence seizures are easily missed by inexperienced observers. Accurate evaluation of their high frequency of recurrence can be a challenge even for experienced observers. We present a novel method for detecting and analyzing absence seizures acquired from electroencephalogram (EEG) recordings in patients with absence seizures. Six patients were included in this study, two seizure free, of a total recording time of 26 hours, and four experiencing over 100 seizures within 14.5 hours of total recordings. Our algorithm detected only one false positive finding in the first seizure free patients and 148 of 186 continuous uninterrupted 3Hz spike and wave discharge (SWD) epochs in the rest of the patients. Out of the total 38 missed SWD epochs 28 were

Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis: A pilot study

Purpose:  Distinguishing nonconvulsive status epilepticus (NCSE) from some nonepileptic encephalopathies is a challenging problem. In many situations, NCSE and nonepileptic encephalopathies are indistinguishable by clinical symptoms and can produce very similar electroencephalography (EEG) patterns. Misdiagnosis or delay to diagnosis of NCSE may increase the rate of morbidity and mortality.

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains

Abstract Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG’s dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis.

Brain network analysis of seizure evolution

The human brain is one of the most complex biological systems. Neuro scientists seek to understand the brain function through detailed analysis of neuronal excitability and synaptic transmission. In this study, we propose a network analysis framework to study the evolution of epileptic seizures. We apply a signal processing approach, derived from information theory, to investigate the synchronization of neuronal activities, which can be captured by electroencephalogram (EEG) recordings. Two network-theoretic approaches are proposed to globally model the synchronization of the brain network. We observe some unique patterns related to the development of epileptic seizures, which can be used to illuminate the brain function governed by the epileptogenic process during the period before a seizure. The proposed framework can provide a global structural patterns in the brain network and may be used in the simulation study of dynamical systems (e.g. the brain) to predict oncoming events (e.g. seizures). To analyze long-term EEG recordings in the future, we discuss how the Markov-Chain Monte Carlo (MCMC) methodology can be applied to estimate the clique parameters. This MCMC framework fits very well with this work as the epileptic evolution can be considered to be a system with unobservable state variables and nonlinearities.

Quantitative analysis on electrooculography (EOG) for neurodegenerative disease

Many studies have documented abnormal horizontal and vertical eye movements in human neurodegenerative disease as well as during altered states of consciousness (including drowsiness and intoxication) in healthy adults. Eye movement measurement may play an important role measuring the progress of neurodegenerative diseases and state of alertness in healthy individuals. There are several techniques for measuring eye movement, Infrared detection technique (IR). Video‐oculography (VOG), Scleral eye coil and EOG. Among those available recording techniques, EOG is a major source for monitoring the abnormal eye movement. In this real‐time quantitative analysis study, the methods which can capture the characteristic of the eye movement were proposed to accurately categorize the state of neurodegenerative subjects. The EOG recordings were taken while 5 tested subjects were watching a short (>120 s) animation clip. In response to the animated clip the participants executed a number of eye movements, including vert...

Optimization of epilepsy treatment with vagus nerve stimulation

Epilepsy is one of the most common chronic neurological disorders that affects close to 50 million people worldwide. Antiepilepsy drugs (AEDs), the main stay of epilepsy treatment, control seizures in two thirds of patients only. Other therapies include the ketogenic diet, ablative surgery, hormonal treatments and neurostimulation. While other approaches to stimulation of the brain are currently in the experimental phase vagus nerve stimulation (VNS) has been approved by the FDA since July 1997 for the adjunctive treatment of intractable partial onset epilepsy with and without secondary generalization in patients twelve years of age or older. The safety and efficacy of VNS have been proven and duplicated in two subsequent double‐blinded controlled studies after two pilot studies demonstrated the feasibility of VNS in man. Long term observational studies confirmed the safety of VNS and that its effectiveness is sustained over time. While AEDs influence seizure thresholds via blockade or modulation of ionic...

A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model

Animal Models are used extensively in basic epilepsy research. In many studies, there is a need to accurately score and quantify all epileptic spike and wave discharges (SWDs) as captured by electroencephalographic (EEG) recordings. Manual scoring of long term EEG recordings is a time-consuming and tedious task that requires inordinate amount of time of laboratory personnel and an experienced electroencephalographer. In this paper, we adapt a SWD detection algorithm, originally proposed by the authors for absence (petit mal) seizure detection in humans, to detect SWDs appearing in EEG recordings of Fischer 334 rats. The algorithm is robust with respect to the threshold parameters. Results are compared to manual scoring and the effect of different threshold parameters is discussed.

Optimisation and data mining techniques for the screening of epileptic patients

Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as ElectroEncephaloGrams (EEGs), is extremely challenging. We propose a novel Multi-Dimensional Time Series (MDTS) classification technique, called Connectivity Support Vector Machines (C-SVMs) that integrates brain connectivity network with SVMs. To alter noise in EEG data, Independent Component Analysis based on the Unbiased Quasi Newton Method was applied. C-SVM achieved 94.8% accuracy classifying subjects compared to 69.4% accuracy with standard SVMs. It suggests that C-SVM can be a rapid, yet accurate, technique for online differentiation between epileptic and normal subjects. It may solve other classification MDTS problems too.

Presence of nonlinearity in intracranial EEG recordings: detected by Lyapunov exponents

In this communication, we performed nonlinearity analysis in the EEG signals recorded from patients with temporal lobe epilepsy (TLE). The largest Lyapunov exponent (Lmax) and phase randomization surrogate data technique were employed to form the statistical test. EEG recordings were acquired invasively from three patients in six brain regions (left and right temporal depth, sub‐temporal and orbitofrontal) with 28–32 depth electrodes placed in depth and subdural of the brain. All three patients in this study have unilateral epileptic focus region on the right hippocampus(RH). Nonlinearity was detected by comparing the Lmax profiles of the EEG recordings to its surrogates. The nonlinearity was seen in all different states of the patient with the highest found in post‐ictal state. Further our results for all patients exhibited higher degree of differences, quantified by paired t‐test, in Lmax values between original and its surrogate from EEG signals recorded from epileptic focus regions. The results of thi...

Subdural recordings from an awake human brain for measuring current intensity during transcranial direct current stimulation

Transcranial direct current stimulation (tDCS) is an emerging method, used for non-invasively stimulating the brain in normal healthy subjects and in patients with neurological disorders. However, the pattern of the spatial distribution of the current intensity induced by tDCS is poorly understood. In this study, we directly measured the spatial characteristics of the current intensity induced by tDCS using an intracranial strip electrode array implanted over the motor cortex in patients with Parkinsons disease undergoing deep brain stimulation lead placement surgery. We used a bilateral stimulation configuration for the tDCS electrode placement and measured the amount of electric current passing through the contacts along the implanted strip electrode contacts. Our results showed significant changes of the current flow induced by the tDCS in some of the contacts during stimulation with respect to baseline activities. These results may provide vital information regarding the biophysical effects of tDCS stimulation and might be potentially useful for developing more effective stimulation strategies.